Non-Invasive Quality Control of Organoid Cultures Using Mesofluidic CSTR Bioreactors and High-Content Imaging.

Human brain organoids produce anatomically relevant cellular structures and recapitulate key aspects of in vivo brain function, which holds great potential to model neurological diseases and screen therapeutics. However, the long growth time of 3D systems complicates the culturing of brain organoids and results in heterogeneity across samples hampering their applications. We developed an integrated platform to enable robust and long-term culturing of 3D brain organoids. We designed a mesofluidic bioreactor device based on a reaction-diffusion scaling theory, which achieves robust media exchange for sufficient nutrient delivery in long-term culture. We integrated this device with longitudinal tracking and machine learning-based classification tools to enable non-invasive quality control of live organoids. This integrated platform allows for sample pre-selection for downstream molecular analysis. Transcriptome analyses of organoids revealed that our mesofluidic bioreactor promoted organoid development while reducing cell death. Our platform thus offers a generalizable tool to establish reproducible culture standards for 3D cellular systems for a variety of applications beyond brain organoids.

Toolkits for detailed and high-throughput interrogation of synapses in C. elegans.

Visualizing synaptic connectivity has traditionally relied on time-consuming electron microscopy-based imaging approaches. To scale the analysis of synaptic connectivity, fluorescent protein-based techniques have been established, ranging from the labeling of specific pre- or post-synaptic components of chemical or electrical synapses to transsynaptic proximity labeling technology such as GRASP and iBLINC. In this paper, we describe WormPsyQi, a generalizable image analysis pipeline that automatically quantifies synaptically localized fluorescent signals in a high-throughput and robust manner, with reduced human bias. We also present a resource of 30 transgenic strains that label chemical or electrical synapses throughout the nervous system of the nematode Caenorhabditis elegans, using CLA-1, RAB-3, GRASP (chemical synapses), or innexin (electrical synapse) reporters. We show that WormPsyQi captures synaptic structures in spite of substantial heterogeneity in neurite morphology, fluorescence signal, and imaging parameters. We use these toolkits to quantify multiple obvious and subtle features of synapses - such as number, size, intensity, and spatial distribution of synapses - in datasets spanning various regions of the nervous system, developmental stages, and sexes. Although the pipeline is described in the context of synapses, it may be utilized for other 'punctate' signals, such as fluorescently tagged neurotransmitter receptors and cell adhesion molecules, as well as proteins in other subcellular contexts. By overcoming constraints on time, sample size, cell morphology, and phenotypic space, this work represents a powerful resource for further analysis of synapse biology in C. elegans.

Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning.

c-Met is a promising target in cancer therapy for its intrinsic oncogenic properties. However, there are currently no c-Met-specific inhibitors available in the clinic. Antibodies blocking the interaction with its only known ligand, hepatocyte growth factor, and/or inducing receptor internalization have been clinically tested. To explore other therapeutic antibody mechanisms like Fc-mediated effector function, bispecific T cell engagement, and chimeric antigen T cell receptors, a diverse panel of antibodies is essential. We prepared a chicken immune scFv library, performed four rounds of bio-panning, obtained 641 clones using a high-throughput clonal retrieval system (TrueRepertoireTM, TR), and found 149 antigen-reactive scFv clones. We also prepared phagemid DNA before the start of bio-panning (round 0) and, after each round of bio-panning (round 1-4), performed next-generation sequencing of these five sets of phagemid DNA, and identified 860,207 HCDR3 clonotypes and 443,292 LCDR3 clonotypes along with their clonal abundance data. We then established a TR data set consisting of antigen reactivity for scFv clones found in TR analysis and the clonal abundance of their HCDR3 and LCDR3 clonotypes in five sets of phagemid DNA. Using the TR data set, a random forest machine learning algorithm was trained to predict the binding properties of in silico HCDR3 and LCDR3 clonotypes. Subsequently, we synthesized 40 HCDR3 and 40 LCDR3 clonotypes predicted to be antigen reactive (AR) and constructed a phage-displayed scFv library called the AR library. In parallel, we also prepared an antigen non-reactive (NR) library using 10 HCDR3 and 10 LCDR3 clonotypes predicted to be NR. After a single round of bio-panning, we screened 96 randomly-selected phage clones from the AR library and found out 14 AR scFv clones consisting of 5 HCDR3 and 11 LCDR3 AR clonotypes. We also screened 96 randomly-selected phage clones from the NR library, but did not identify any AR clones. In summary, machine learning algorithms can provide a method for identifying AR antibodies, which allows for the characterization of diverse antibody libraries inaccessible by traditional methods.

High-throughput retrieval of physical DNA for NGS-identifiable clones in phage display library.

In antibody discovery, in-depth analysis of an antibody library and high-throughput retrieval of clones in the library are crucial to identifying and exploiting rare clones with different properties. However, existing methods have technical limitations, such as low process throughput from the laborious cloning process and waste of the phenotypic screening capacity from unnecessary repetitive tests on the dominant clones. To overcome the limitations, we developed a new high-throughput platform for the identification and retrieval of clones in the library, TrueRepertoire™. This new platform provides highly accurate sequences of the clones with linkage information between heavy and light chains of the antibody fragment. Additionally, the physical DNA of clones can be retrieved in high throughput based on the sequence information. We validated the high accuracy of the sequences and demonstrated that there is no platform-specific bias. Moreover, the applicability of TrueRepertoire™ was demonstrated by a phage-displayed single-chain variable fragment library targeting human hepatocyte growth factor protein.

Efficient Selection of Antibodies Reactive to Homologous Epitopes on Human and Mouse Hepatocyte Growth Factors by Next-Generation Sequencing-Based Analysis of the B Cell Repertoire.

: YYB-101 is a humanized rabbit anti-human hepatocyte growth factor (HGF)-neutralizing antibody currently in clinical trial. To test the effect of HGF neutralization with antibody on anti-cancer T cell immunity, we generated surrogate antibodies that are reactive to the mouse homologue of the epitope targeted by YYB-101. First, we immunized a chicken with human HGF and monitored changes in the B cell repertoire by next-generation sequencing (NGS). We then extracted the VH gene repertoire from the NGS data, clustered it into components by sequence homology, and classified the components by the change in the number of unique VH sequences and the frequencies of the VH sequences within each component following immunization. Those changes should accompany the preferential proliferation and somatic hypermutation or gene conversion of B cells encoding HGF-reactive antibodies. One component showed significant increases in the number and frequencies of unique VH sequences and harbored genes encoding antibodies that were reactive to human HGF and competitive with YYB-101 for HGF binding. Some of the antibodies also reacted to mouse HGF. The selected VH sequences shared 98.3% identity and 98.9% amino acid similarity. It is therefore likely that the antibodies encoded by them all react to the epitope targeted by YYB-101.